Chapter 2 Data assembly

The following chapter details how we determined the area of interest and where we acquired the data products to be used in the connectivity analyses. We include justification of data sources where appropriate.

2.1 Define the focal area

This project focuses on the area covered by the Mesoamerican Biological Corridor MBC, which spans most of mainland central America. This excludes islands in central America and the Caribbean, as these will likely need a differing set of ridge-to-reef definitions. We also exclude Mexico.

The full focal area spans:

The area in orange is the region which we aquire data for, the red area represents the regions for which we explore the connectivity. The reason for the expansion is to minimise the possibility of edge effects.

Below we show the exclusion of surrounding islands:

For an up to date assessment of the Mesoamerican Biological Corridor in Panama alone see:

Meyer, N. F., Moreno, R., Reyna-Hurtado, R., Signer, J., & Balkenhol, N. (2020). Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models. Movement ecology, 8(1), 1-14.

2.2 Data products

2.2.1 Protected areas

Shape files for protected areas were downloaded from the Protected Planet database. The World Database on Protected Areas (WDPA) is the most up-to-date and complete source of information on protected areas, updated monthly with submissions from governments, non-governmental organizations, landowners, and communities. It is managed by the United Nations Environment Programme’s World Conservation Monitoring Centre (UNEP-WCMC) with support from IUCN and its World Commission on Protected Areas (WCPA).

We buffered the area of interest by 10km, then excluded any protected ares which fell outside of that zone. This means marine protected areas >10km from the shore are not considered. There are two broad types of park - National and International designations - there are also many further subdivisions not considered here.

As recommended in the WDPA best practices guide, we removed any PA that did not report its area, or with a ‘Proposed’ status or ‘UNESCO-MAB Biosphere Reserve’ designation (Note core areas remain under national park designations).

We now also use the package wdpar to clean known issues in the database (i.e. filter out overlapping sections).

Data source: UNEP-WCMC and IUCN (year), Protected Planet: The World Database on Protected Areas (WDPA) [February 2022], Cambridge, UK: UNEP-WCMC and IUCN Available at: Protected Planet.

And and all protected areas in an interactive version:

2.2.1.1 Types of protected area

Within our focal area, the WPDA dataset includes the following number of terrestrial and marine protected areas:

Var1	Freq
marine	13
partial	53
terrestrial	668

There are also a myriad of different protection designations:

Type	Freq
Archaeological Reserve	4
Area de Manejo de Hábitat	1
Área de Manejo de Hábitat	3
Area de Manejo de Hábitat/Especies	7
Área de Manejo de Hábitat/Especies	1
Área de Protección de Flora y Fauna	1
Área de Protección y Restauración	4
Área de Recursos Manejados	2
Area de Uso Multiple	4
Area de Uso Múltiple	4
Área de Uso Múltiple	2
Area Marina de Manejo	2
Área Natural	3
Área Natural Protegida	22
Área Natural Protegida Privada	2
Área Productora de Agua	2
Área Protegida con Recursos Manejados	3
Área Recreativa	2
Área Silvestre	2
Biotopo Protegido	6
Bosque Protector	2
Bosque Protector y Paisaje Protegido	2
Burdon Canal Nature Reserve	1
Cockscomb Basin Wildlife Sanctuary	1
Conservation Easement	1
Corredor Biológico	1
Crooked Tree Wildlife Sanctuary	1
Forest Reserve	16
Humedal	5
Jardín Botánico y Centro de Investigación	1
Labouring Creek Jaguar Corridor Wildlife Sanctuary	1
Monumento Cultural	4
Monumento Historico	1
Monumento Nacional	3
Monumento Natural	13
Monumento Natural Marino	1
Mountain Pine Ridge Forest Reserve	1
National Park	13
Natural Monument	3
Nature Reserve	3
Nohoch Cheen Archaeological Reserve	1
Paisaje Protegido	4
Paisaje Terrestre Protegido	11
Parque Ecológico	1
Parque Nacional	85
Parque Nacional Natural	1
Parque Regional	1
Parque Regional Municipal	46
Parque Regional y Área Natural Recreativa	1
Private Reserve	8
Ramsar Site, Wetland of International Importance	23
Refugio de Vida Silvestre	31
Refugio Nacional de Vida Silvestre	43
Reserva Antropológica y Forestal	1
Reserva Biologica	4
Reserva Biológica	13
Reserva Biologica Marina	1
Reserva Biósfera	2
Reserva de la Biosfera	3
Reserva de la Biósfera	2
Reserva de Recursos Genéticos	2
Reserva de Uso Multiple	1
Reserva Forestal	15
Reserva Forestal Municipal	2
Reserva Forestal Protectora de Manantiales	1
Reserva Hídrica	3
Reserva Hídrica y Forestal	1
Reserva Hidrológica	3
Reserva Natural	52
Reserva Natural Absoluta	1
Reserva Natural Privada	127
Reserva Protectora de Manantiales	1
Reservas Forestales Protectoras Nacionales	1
Reservas Naturales Privadas	9
Sin definir	1
Sitio Ramsar, Humedal de Importancia Internacional	7
Wildlife Sanctuary	3
World Heritage Site (natural or mixed)	5
Zona de Protección Hidrológica	1
Zona de Reserva Ecológica	2
Zona de Veda Definitiva	30
Zona Protectora	30
Zona Sujeta a Conservación Ecológica	3

2.2.2 Elevation

We downloaded the elevation of the area of interest using SRTM Digital Elevation Data Version 4. The Shuttle Radar Topography Mission (SRTM) digital elevation dataset was originally produced to provide consistent, high-quality elevation data at near global scope.

Data source: Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara. 2008. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database.

2.2.3 Forest cover (current)

To get a layer reflecting current forest cover we use the Hansen Global forest Change index. These reflect results from time-series analysis of Landsat images in characterizing global forest extent and change. This data is up to date until 2021!

Possible future data incorporation:

Current (and future) coarse vegetation types can also be obtained from this Baumbach, L., Warren, D. L., Yousefpour, R., & Hanewinkel, M. (2021). Climate change may induce connectivity loss and mountaintop extinction in Central American forests. Communications Biology, 4(1), 1-12.

For an example approaches in modelling connectivity in the future (beyond the remit of this contract) see: Mozelewski, T. G., Robbins, Z. J., Scheller, R. M., & Mozelewski, T. G. (2022). Forecasting the influence of conservation strategies on landscape connectivity. Conservation Biology

2.2.4 Forest biomass

We obtained above ground biomass from Spawn, S.A., Sullivan, C.C., Lark, T.J. et al. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci Data 7, 112 (2020). This dataset provides temporally consistent and harmonized global maps of above-ground and below-ground biomass carbon density for the year 2010 at a 300-m spatial resolution.

The values represent above-ground living biomass carbon stock density of combined woody and herbaceous cover in 2010. This includes carbon stored in living plant tissues that are located above the earth’s surface (stems, bark, branches, twigs). This does not include leaf litter or coarse woody debris that were once attached to living plants but have since been deposited and are no longer living.

2.2.5 Forest height

For forest height we use the global 2005 dataset representing global tree heights based on a fusion of spaceborne-lidar data (2005) from the Geoscience Laser Altimeter System (GLAS) and ancillary geospatial data. See Simard et al. (2011) for details.

Simard, M., Pinto, N., Fisher, J., Baccini, A. 2011. Mapping forest canopy height globally with spaceborne lidar. Journal of Geophysical Research. 116: G04021

2.2.6 Mangrove cover (current)

Mangrove data is taken from the [USGS: Global Distribution of Mangroves] (https://data.unep-wcmc.org/datasets/4).

Citation: Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011). Status and distribution of mangrove forests of the world using earth observation satellite data (version 1.4, updated by UNEP-WCMC). Global Ecology and Biogeography 20: 154-159. Paper DOI: 10.1111/j.1466-8238.2010.00584.x . Data DOI: https://doi.org/10.34892/1411-w728

2.2.7 Human disturbance:

To incorporate human disturbance into the connectivity analyses we use the global Human Modification dataset (gHM). This dataset provides a cumulative measure of human modification of terrestrial lands globally at 1 square-kilometer resolution. The gHM values range from 0.0-1.0 and are calculated by estimating the proportion of a given location (pixel) that is modified, the estimated intensity of modification associated with a given type of human modification or “stressor”. They mapped 5 major anthropogenic stressors circa 2016 were mapped using 13 individual datasets:

• human settlement (population density, built-up areas)
• agriculture (cropland, livestock)
• transportation (major, minor, and two-track roads; railroads)
• mining and energy production
• electrical infrastructure (power lines, nighttime lights)

As such, this layer represents a great starting point to measure broad scale patterns in elevational gradient disturbances.

Kennedy, C.M., J.R. Oakleaf, D.M. Theobald, S. Baurch-Murdo, and J. Kiesecker. 2019. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology 00:1-16.

Examples of papers usuing the human modification index (or a derivation of it): - Gray M, Micheli E, Comendant T, Merenlender A (2020) Quantifying climate-wise connectivity across a topographically diverse landscape. Land 9:1–18. https://doi.org/10.3390/land9100355 This paper does terrestrial and riparian “permeability” - the inverse of resistance. They then compare the “cooling potential” of these corridors through looking at the pairwise difference between temperatures between linkages.

2.2.8 Current land-use and habitat

To capture land use, we are used the high resolution (10m) land cover map over Mexico and Central America created by ESA (ESA/WorldCover/v200). The data are based on more than 2 years of Sentinel-2A and 2B observations from January 2016 to March 2018.

2.3 Start and end nodes

The analysis presented in the following chapters depends on having a suite of meaning start locations “reef” (reflecting mangroves, coastal protected areas and marine protected areas) and end locations “ridges” (reflecting protected high elevation forest habitats). The following code outlines the candidate start and end locations.

2.3.1 Start nodes

This is where the connectivity paths will start from:

2.3.1.1 Low elevation protected areas

We will use start locations which represent lowland protected areas which have elevations <500m, above that and we start to transition into sub-montane forests types. Note - we unionize adjacent protected areas, then filter our isolated protected areas which are small in size < 3 km2, the double the critical habitat island size for collared peccaries, from:

Benchimol, Maíra, and Carlos A. Peres. “Predicting local extinctions of Amazonian vertebrates in forest islands created by a mega dam.” Biological Conservation 187 (2015): 61-72.

Reading layer `lowland_protected_areas' from data source `C:\Users\cwbei\Dropbox\GitHubProjects\Osa-Conservation-Connectivity-Project\data\spatial\protected_areas\lowland_protected_areas.shp' using driver `ESRI Shapefile'
Simple feature collection with 684 features and 35 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -96.06792 ymin: 3.64628 xmax: -73.46516 ymax: 21.61603
Geodetic CRS:  WGS 84

So we have around 684 protected areas which are link ocean and land.

2.3.2 End nodes

End nodes represent where the connectivity paths will terminate.

2.3.2.1 High elevation protected areas

When we are measuring Ridge to Reef connectivity - we need to define a height threshold that represents a meaningful transition in elevation. What should this height be? We have initiated the analysis using a 1500m threshold, but why not use 1000m? Where we draw the line potentially influences the availability of high elevation areas which animals can move to.

Below we provide graphical representation of different height thresholds across the area of interest:

Examples of studies in central America examining elevation gradients:

Smith MA, Hallwachs W, Janzen DH (2014) Diversity and phylogenetic community structure of ants along a Costa Rican elevational gradient. Ecography (Cop) 37:720–731. https://doi.org/10.1111/j.1600-0587.2013.00631.x A study on ants - consdered “high elevation” to be between 1300 and 1600m and found high uniqueness in those high elevation sites.

From: Neate-Clegg MHC, Jones SEI, Burdekin O, et al (2018) Elevational changes in the avian community of a Mesoamerican cloud forest park. Biotropica 50:805–815. https://doi.org/10.1111/btp.12596

“Over a 10-year period, we found general increases in avian species richness and diversity at mid-to-high elevations (>1200 m), but declines at low elevations. This suggests upslope shifts in the community with lowland biotic attrition (Colwell et al. 2008)”

Deleting layer `high_elevation_pas' using driver `ESRI Shapefile'
Writing layer `high_elevation_pas' to data source `data/spatial/area_of_interest/high_elevation_pas.shp' using driver `ESRI Shapefile'
Writing 333 features with 34 fields and geometry type Unknown (any).

Deleting layer `high_elevation_pas_contiguous' using driver `ESRI Shapefile'
Writing layer `high_elevation_pas_contiguous' to data source `data/spatial/area_of_interest/high_elevation_pas_contiguous.shp' using driver `ESRI Shapefile'
Writing 517 features with 35 fields and geometry type Unknown (any).

We will use high elevation (>1500) protected areas as the “end” points for our connectivity analyses.

2.3.3 Existing corridors

2.3.3.1 Corridors according to CBM

Data obtained from the Central American Commission on Environment and Development (CCAD) - indirectly through PhD researcher Ruchi Patel rdp20@psu.edu - based on her paper: Patel, R. (2021). Paper plans and possibility: A critical analysis of landscape conservation policy in the Mesoamerican Biological Corridor. Environmental Development, 37, 100600.

The corridors span the following countries:

        Belize     Costa Rica    El Salvador      Guatemala       Honduras         Mexico      Nicaragua         Panama Zona Froteriza 
           288            560           1229            385            212            158            452            530             46 

2.3.4 Other sources

2.3.4.1 Key Biodiversity Areas

Key Biodiversity Areas (KBAs) are sites of global significance for the conservation of biodiversity. Currently there are 15,524 KBAs acknowledged worldwide, and more are continue to be identified nationally using simple, globally standardised criteria and thresholds, based on biodiversity requiring safeguards at the site scale. There are 11 criteria organized into five categories, namely (1) threatened biodiversity, (2) geographically restricted biodiversity, (3) ecological integrity, (4) biological processes, and (5) irreplaceability. As the building blocks for designing the ecosystem, bottom-up approach and maintaining effective ecological networks, Key Biodiversity Areas are the starting point for landscape-level conservation planning.

We will ultimately explore the intersection between our ridge-to-reef corridors and these designated areas.

Costa Rica: SINAC http://www.sinac.go.cr/EN-US/correbiolo/Pages/default.aspx